A simple genetic algorithm for tracing the deformed midline on a single slice of brain CT using quadratic Bézier curves

Chun Chih Liao, Furen Xiao, Jau Min Wong, I-Jen Chiang

研究成果: 書貢獻/報告類型會議貢獻

12 引文 斯高帕斯(Scopus)

摘要

Midline shift (MLS) is one of the most important quantitative features clinicians use to evaluate the severity of brain compression. It can be recognized by modeling brain deformation according to the estimated biomechanical properties of the brain structures. This paper proposes a novel method to identify the deformed midline by decomposing it into three segments: the upper and the lower straight segments representing parts of the tough meninges separating two brain hemispheres, and the central curved segment formed by a quadratic Bézier curve, representing the intervening soft brain tissue. The deformed midline is obtained by minimizing the summed square of the differences across all midline points, applying a genetic algorithm. Our algorithm was evaluated on images containing various pathologies from 81 consecutive patients treated in a single institute over one-year period. The deformed midlines were evaluated by human experts, and the values of midline shift were accurate in 95%.
原文英語
主出版物標題Proceedings - IEEE International Conference on Data Mining, ICDM
頁面463-467
頁數5
出版狀態已發佈 - 2006
事件6th IEEE International Conference on Data Mining - Workshops, ICDM 2006 - Hong Kong, 中国
持續時間: 十二月 18 2006十二月 18 2006

其他

其他6th IEEE International Conference on Data Mining - Workshops, ICDM 2006
國家中国
城市Hong Kong
期間12/18/0612/18/06

ASJC Scopus subject areas

  • Engineering(all)

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